Fraction-variant beam orientation optimization for non-coplanar IMRT

Conventional beam orientation optimization (BOO) algorithms for IMRT assume that the same set of beam angles is used for all treatment fractions. In this paper we present a BOO formulation based on group sparsity that simultaneously optimizes non-coplanar beam angles for all fractions, yielding a fraction-variant (FV) treatment plan. Beam angles are selected by solving a multi-fraction FMO problem involving 500-700 candidate beams per fraction, with an additional group sparsity term that encourages most candidate beams to be inactive. The optimization problem is solved using the Fast Iterative Shrinkage-Thresholding Algorithm. Our FV BOO algorithm is used to create non-coplanar, five-fraction treatment plans for prostate and lung cases, as well as a non-coplanar 30-fraction plan for a head and neck case. A homogeneous PTV dose coverage is maintained in all fractions. The treatment plans are compared with fraction-invariant plans that use a fixed set of beam angles for all fractions. The FV plans reduced mean and max OAR dose on average by 3.3% and 3.7% of the prescription dose, respectively. Notably, mean OAR dose was reduced by 14.3% of prescription dose (rectum), 11.6% (penile bulb), 10.7% (seminal vesicle), 5.5% (right femur), 3.5% (bladder), 4.0% (normal left lung), 15.5% (cochleas), and 5.2% (chiasm). Max OAR dose was reduced by 14.9% of prescription dose (right femur), 8.2% (penile bulb), 12.7% (prox. bronchus), 4.1% (normal left lung), 15.2% (cochleas), 10.1% (orbits), 9.1% (chiasm), 8.7% (brainstem), and 7.1% (parotids). Meanwhile, PTV homogeneity defined as D95/D5 improved from .95 to .98 (prostate case) and from .94 to .97 (lung case), and remained constant for the head and neck case. Moreover, the FV plans are dosimetrically similar to conventional plans that use twice as many beams per fraction. Thus, FV BOO offers the potential to reduce delivery time for non-coplanar IMRT

Cux2-Positive Radial Glial Cells Generate Diverse Subtypes of Neocortical Projection Neurons and Macroglia

The mammalian neocortex is 6-layered structure that develops in an “inside-out” manner, with cells of the deep layers (Layers 5-6) born first. Cells of the superficial outer layers (Layers 2-4) are generated subsequently and must migrate past older born cells to their final laminar position. Pioneering transplant studies suggested a progressive lineage restriction model, which posits that early neural stem cells (or radial glial cells, RGCs) are multipotent and sequentially generate different types of cortical neurons based on birthdate. Recently published work from Franco et al. (2012) argues against this paradigm, and proposes the existence of a subclass of neural stem cells, fated from an early embryonic age to produce exclusively upper-layer neurons. They contend that at mouse embryonic day 10.5 (E10.5), when neocortical genesis is just beginning, an RGC subpopulation marked by expression of genetic transcription factor cut-like homeobox 2 (Cux2) is fated to produce exclusively upper layer (L2-4) cells. Cells not expressing Cux2 are fated to become deep layer (L5-6). We recently published work testing this model using Cre-mediated recombination. Our experiments demonstrated that both clonal and population levels of Cux2+ and Fezf2+ RGCs produce progeny that are multipotent and able to generate neurons, astrocytes, and oligodendrocytes. Here, we extend our lab’s previous work of the Cux2-positive and Fezf2-positive RGC lineages and find that E10.5 neocortical progenitors are able to generate diverse neuronal subtypes located throughout layers 2-6 as well as macroglia. Collectively, we find that Cux2-positive RGCs development does not differ from the progressive lineage restriction theory, and does not support the cell-intrinsic theory postulated by Franco et al. (2012)

Success Factors as Critical That Shape Agile Software Development Project Success

This study has implications for positive social change because organizations that understand the critical factors may be able to improve project management strategies and cost benefits leading to higher efficiency, profitability, and productivity thus benefiting management, employees, and customers. Information technology (IT) project success depends on having a project manager with effective decision-making, leadership, and project management skills. Project success also depends on completing the project in a given budget, time, and scope. However, there is a limited understanding of the lived experiences of agile managers and the following success factors: engineering, management, organization, and stakeholders. The purpose of this phenomenological study was to understand these lived experiences of 10 agile software development team project managers or leaders at global workplaces based in the United States. The research questions were focused on the effect of these success factors on agile software development project success. In accordance with nonrandom purposeful sampling strategies, a snowball technique was used to find more participants. An open-ended, e-mail questionnaire was created and sent to participants to collect data. The data were coded to discern themes or patterns. According to study results, agile software development team employs automate builds, continuous integration, and design patterns help reduce technical debt; good collaboration and communication skills are core to project success; product owner helps maximize business value delivered by team and priority and engage stakeholders; and sponsors help fund the project and other resources

Escargot

The nonlinear optical characteristics of semiconductors are studied near the two-photon biexciton resonance. Optical bistability is shown to happen below this resonance at very low light intensities

Human Rights Treaty Commitment and Compliance: A Machine Learning-based Causal Inference Approach

Why do states ratify international human rights treaties? How much do human rights treaties influence state behaviors directly and indirectly? Why are some human rights treaty monitoring procedures more effective than others? What are the most predictively and causally important factors that can reduce and prevent state repression and human rights violations? This dissertation provide answers to these keys causal questions in political science research, using a novel approach that combines machine learning and the structural causal model framework. The four research questions are arranged in a chronological order that refects the causal process relating to international human rights treaties, going from (a) the causal determinants of treaty ratification to (b) the causal mechanisms of human rights treaties to (c) the causal effects of human rights treaty monitoring procedures to (d) other factors that causally influence human rights violations. Chapter 1 identifies the research traditions within which this dissertation is located, offers an overview of the methodological advances that enable this research, specifies the research questions, and previews the findings. Chapters 2, 3, 4, and 5 present in chronological order four empirical studies that answer these four research questions. Finally, Chapter 6 summarizes the substantive findings, suggests some other research questions that could be similarly investigated, and recaps the methodological approach and the contributions of the dissertation